December 2025 arXiv papers — page 17

Yangyang Zhang

We study the relation between semipositivity, nefness, and bigness of line bundles on compact K\"ahler manifolds. Every nef and big line bundle on a compact K\"ahler manifold $X$ is positive when ${\rm dim}\,X = 1$. Kim constructed an explicit example of a nef and big line bundle that is not semipositive in the case ${\rm dim}\,X \ge 3$. Motivated by a conje

Manuel Franco-Vivo

As autonomous vehicle technology advances, ensuring the safety and reliability of these systems becomes paramount. Consequently, comprehensive testing methodologies are essential to evaluate the performance of autonomous vehicles in diverse and complex real-world scenarios. This study focuses on the behaviour coverage analysis of a multi-agent system simulat

Zina-Sabrina Duma, Otto Lamminpää, Jouni Susiluoto, Heikki Haario

Uncertainty quantification is essential for scientific analysis, as it allows for the evaluation and interpretation of variability and reliability in complex systems and datasets. In their original form, multivariate statistical regression models (partial least-squares regression, PLS, principal component regression, PCR) along with their kernelized versions

Jiada Huang, Hao Ma, Zhibin Shen, Yizhou Qiao

Local high strain in solid rocket motor grains is a primary cause of structural failure. However, traditional numerical simulations are computationally expensive, and existing surrogate models cannot explicitly establish geometric models and accurately capture high-strain regions. Therefore, this paper proposes an adaptive graph network, GrainGNet, which emp

Ruriko Yoshida, Zhiwen Wang

A kernel density estimator (KDE) is one of the most popular non-parametric density estimators. In this paper we focus on a best bandwidth selection method for use in an analogue of a classical KDE using the tropical symmetric distance, known as a tropical KDE, for use over the space of phylogenetic trees. We propose the likelihood cross validation (LCV) for

Taha Emre, Arunava Chakravarty, Thomas Pinetz, Dmitrii Lachinov

Temporally aware image representations are crucial for capturing disease progression in 3D volumes of longitudinal medical datasets. However, recent state-of-the-art self-supervised learning approaches like Masked Autoencoding (MAE), despite their strong representation learning capabilities, lack temporal awareness. In this paper, we propose STAMP (Stochasti

Yuqi Tang, Jing Yu, Zichang Su, Kehua Feng

Clinical diagnosis begins with doctor-patient interaction, during which physicians iteratively gather information, determine examination and refine differential diagnosis through patients' response. This dynamic clinical-reasoning process is poorly represented by existing LLM benchmarks that focus on static question-answering. To mitigate these gaps, recent

Johannes Lenzen, Mohamadreza Rostami, Lichao Wu, Ahmad-Reza Sadeghi

Modern CPUs are black boxes, proprietary, and increasingly characterized by sophisticated microarchitectural flaws that evade traditional analysis. While some of these critical vulnerabilities have been uncovered through cumbersome manual effort, building an automated and systematic vulnerability detection framework for real-world post-silicon processors rem

Shuhong Liu, Chenyu Bao, Ziteng Cui, Yun Liu

We introduce RealX3D, a real-capture benchmark for multi-view visual restoration and 3D reconstruction under diverse physical degradations. RealX3D groups corruptions into four families, including illumination, scattering, occlusion, and blurring, and captures each at multiple severity levels using a unified acquisition protocol that yields pixel-aligned LQ/

Mustafa Demetgul, Sanja Lazarova Molnar

Monitoring states of road surfaces provides valuable information for the planning and controlling vehicles and active vehicle control systems. Classical road monitoring methods are expensive and unsystematic because they require time for measurements. This article proposes an real time system based on weather conditional data and road surface condition data.

Saifelden M. Ismail

Speech Emotion Recognition (SER) has significant potential for mobile applications, yet deployment remains constrained by the computational demands of state-of-the-art transformer architectures. This paper presents a mobile-efficient SER system based on DistilHuBERT, a distilled and 8-bit quantized transformer that achieves approximately 92% parameter reduct

Yongjie Guan

Consistent hashing is fundamental to distributed systems, but ring-based schemes can exhibit high peak-to-average load ratios unless they use many virtual nodes, while multi-probe methods improve balance at the cost of scattered memory accesses. This paper introduces Local Rendezvous Hashing (LRH), which preserves a token ring but restricts Highest Random We

Yurii V. Dumin, Ludmila M. Svirskaya, Eugen S. Savinykh

The efficiency of recombination is of crucial importance for the existence of ultracold plasmas (UCP), particularly, the ones formed in the magneto-optical traps. Unfortunately, the equilibrium thermodynamic treatment of the ionization-recombination processes is inappropriate for the evolving UCP clouds, while the straightforward kinetic simulation encounter

Simay Atasoy Bingöl, Tobias Töpfer, Sven Kosub, Heiko Hamann

In collective systems, the available agents are a limited resource that must be allocated among tasks to maximize collective performance. Computing the optimal allocation of several agents to numerous tasks through a brute-force approach can be infeasible, especially when each task's performance scales differently with the increase of agents. For example, di

Xuan Feng, Bo An, Tianlong Gu, Liang Chang

Bias in Large Language Models (LLMs) poses significant risks to trustworthiness, manifesting primarily as stereotypical biases (e.g., gender or racial stereotypes) and structural biases (e.g., lexical overlap or position preferences). However, prior paradigms typically address these in isolation, often mitigating one at the expense of exacerbating the other.

Yi Zhao, Yongjun Zhu, Donghun Kim, Yuzhuo Wang

The influence of gender diversity on the success of scientific teams is of great interest to academia. However, prior findings remain inconsistent, and most studies operationalize diversity in aggregate terms, overlooking internal role differentiation. This limitation obscures a more nuanced understanding of how gender diversity shapes team impact. In partic

Hrishav Das, Devendra K. Sahu, Anirban Dutta, Mridweeka Singh

We present comprehensive photometric and spectroscopic observations of Supernova (SN) 2022eyw, a luminous member of the Type Iax SN subclass. SN 2022eyw reached a peak absolute magnitude of $M_g = -17.80\pm0.15$ mag and exhibited a rise time of $\sim$15 days, placing it among the brighter Iax events. The bolometric light curve indicates a synthesized $^{56}$

Jesse Brouwers, Xiaoyan Xing, Alexander Timans

Foundation models for segmentation such as the Segment Anything Model (SAM) family exhibit strong zero-shot performance, but remain vulnerable in shifted or limited-knowledge domains. This work investigates whether uncertainty quantification can mitigate such challenges and enhance model generalisability in a domain-agnostic manner. To this end, we (1) curat

Amedeo M. Favitta

The post-inflationary Peccei-Quinn symmetry-breaking scenario provides a rich theoretical framework to study axion dark matter production through the dynamics oftopological defects. Accurate predictions for the axion abundance require a detailed understanding of the formation and evolution of cosmic strings and domain walls, which are inevitably produced in

William Kengne, Modou Wade

This paper develops a general approach for deep learning for a setting that includes nonparametric regression and classification. We perform a framework from data that fulfills a generalized Bernstein-type inequality, including independent, $\phi$-mixing, strongly mixing and $\mathcal{C}$-mixing observations. Two estimators are proposed: a non-penalized deep

Jinye Du, Quan Yuan, Zuyao Zhang, Yanzhi Yi

Modern AI models demand high-performance computation kernels. The growing complexity of LLMs, multimodal architectures, and recommendation systems, combined with techniques like sparsity and quantization, creates significant computational challenges. Moreover, frequent hardware updates and diverse chip architectures further complicate this landscape, requiri

Wei Cheng

This paper constructs and analyzes a three channel dissipative framework for Warm Higgs Inflation, wherein the total dissipation coefficient, $\Upsilon(h,T)$, is decomposed into low temperature, high temperature, and threshold activated contributions. A genetic algorithm is employed for the global numerical solution and statistical inference of the backgroun

Jiapeng Wang, Yiwen Hu, Yanzipeng Gao, Haoyu Wang

As access to high-quality, domain-specific data grows increasingly scarce, multi-epoch training has become a practical strategy for adapting large language models (LLMs). However, autoregressive models often suffer from performance degradation under repeated data exposure, where overfitting leads to a marked decline in model capability. Through empirical ana

Tianze Xia, Yongkang Li, Lijun Zhou, Jingfeng Yao

World models have become crucial for autonomous driving, as they learn how scenarios evolve over time to address the long-tail challenges of the real world. However, current approaches relegate world models to limited roles: they operate within ostensibly unified architectures that still keep world prediction and motion planning as decoupled processes. To br

Antika Yadav, Prasad Vilas Chanekar

In this paper we study the control co-design (CCD) synthesis problem for a class of systems with parabolic partial differential equation (PDE) dynamics. We formulate CCD problem and finally derive an approximate CCD problem with matrix algebraic constraint. We then solve this approximate problem with gradient-based method and prove that the optimal solution

Alex Lewandowski, Adtiya A. Ramesh, Edan Meyer, Dale Schuurmans

Continual learning is often motivated by the idea, known as the big world hypothesis, that "the world is bigger" than the agent. Recent problem formulations capture this idea by explicitly constraining an agent relative to the environment. These constraints lead to solutions in which the agent continually adapts to best use its limited capacity, rather than

Navdeep Singh Dhindsa, Debsubhra Chakraborty, Archana Radhakrishnan, Nilmani Mathur

We present the most precise determination to date of the ground-state masses of the triply charmed baryons with both parities, obtained by continuum extrapolation and fully addressing the systematic uncertainties. The calculations are performed on six $N_f=2+1+1$ HISQ ensembles, generated by the MILC collaboration, with two complementary setups for the valen

Sandeep Kumar Mondal, Shubham Kishore, Alok C. Gupta, Gwenael Giacinti

In this work, we report evidence suggesting the potential future detection of a month-scale quasi-periodic oscillation (QPO) in the gamma-ray light curve of OP 313. We analysed almost 16.8 years of Fermi-LAT gamma-ray data and applied the Bayesian block method to the monthly-binned light curve. We identified four high-flux states and investigated the possibi

Vinoth Punniyamoorthy, Bikesh Kumar, Sumit Saha, Lokesh Butra

Kubernetes provides native autoscaling mechanisms, including the Horizontal Pod Autoscaler, Vertical Pod Autoscaler, and node-level autoscalers, to enable elastic resource management for cloud-native applications. However, production environments frequently experience Service Level Objective violations and cost inefficiencies due to reactive scaling behavior

Zheng Li

In arXiv:2405.04947, it was shown that a Gaussian quantum Markov semigroup on the $d$-mode bosonic Fock space with a unique faithful normal invariant state has a positive GNS spectral gap if and only if the matrix $[U \overline{V}]$, formed from the coefficients of the Kraus operators, has $2d$ linearly independent columns. In this paper, we establish the co

Henglin Liu, Nisha Huang, Chang Liu, Jiangpeng Yan

The aesthetic quality assessment task is crucial for developing a human-aligned quantitative evaluation system for AIGC. However, its inherently complex nature, spanning visual perception, cognition, and emotion, poses fundamental challenges. Although aesthetic descriptions offer a viable representation of this complexity, two critical challenges persist: (1

Jiawei Chen, Xintian Shen, Lihao Zheng, Zhenwei Shao

Traditional workflow-based agents exhibit limited intelligence when addressing real-world problems requiring tool invocation. Tool-integrated reasoning (TIR) agents capable of autonomous reasoning and tool invocation are rapidly emerging as a powerful approach for complex decision-making tasks involving multi-step interactions with external environments. In

Xiaolan Li, Wanquan Liu, Pengcheng Li, Pengyu Jie

Three-dimensional (3D) tooth instance segmentation remains challenging due to crowded arches, ambiguous tooth-gingiva boundaries, missing teeth, and rare yet clinically important third molars. Native 3D methods relying on geometric cues often suffer from boundary leakage, center drift, and inconsistent tooth identities, especially for minority classes and co

Yusuf Kalyoncuoglu, Ratmir Miftachov

State-of-the-art models rely on massive widths despite exhibiting low Intrinsic Dimension (ID). We posit that this redundancy serves the non-convex optimization search rather than the final representation. We validate this hypothesis by decoupling the solution geometry via data-independent random projections, demonstrating that ResNet, ViT, and BERT represen

Youichiro Higashi, Kemal Ozbek, Norio Takeoka

In this paper, we study axiomatic foundations of Bayesian persuasion, where a principal (i.e., sender) delegates the task of choice making after informing a biased agent (i.e., receiver) about the payoff relevant uncertain state (see, e.g., Kamenica and Gentzkow (2011)). Our characterizations involve novel models of Bayesian persuasion, where the principal c

Yong Chen, Jiayi Tong, Yiwen Lu, Rui Duan

Background: Distributed Research Networks (DRNs) offer significant opportunities for collaborative multi-site research and have significantly advanced healthcare research based on clinical observational data. However, generating high-quality real-world evidence using fit-for-use data from multi-site studies faces important challenges, including biases associ

Bruno Mlodozeniec, David Krueger, Richard E. Turner

Causal inference is a key research area in machine learning, yet confusion reigns over the tools needed to tackle it. There are prevalent claims in the machine learning literature that you need a bespoke causal framework or notation to answer causal questions. In this paper, we want to make it clear that you \emph{can} answer any causal inference question wi

Huan Song, Qingfei Zhao, Ting Long, Shuyu Tian

Neural scaling laws have become foundational for optimizing large language model (LLM) training, yet they typically assume a single dense model output. This limitation effectively overlooks "Familial models, a transformative paradigm essential for realizing ubiquitous intelligence across heterogeneous device-edge-cloud hierarchies. Transcending static archit

Vinoth Punniyamoorthy, Kabilan Kannan, Akshay Deshpande, Lokesh Butra

API gateways serve as critical enforcement points for security, governance, and traffic management in cloud-native systems. As organizations increasingly adopt multi-cluster and hybrid cloud deployments, maintaining consistent policy enforcement, predictable performance, and operational stability across heterogeneous gateway environments becomes challenging.

Ayushman Raghuvanshi, Gonzalo Mateos, Sundeep Prabhakar Chepuri

Graph neural networks (GNNs) often struggle to learn discriminative node representations for heterophilic graphs, where connected nodes tend to have dissimilar labels and feature similarity provides weak structural cues. We propose frequency-guided graph structure learning (FgGSL), an end-to-end graph inference framework that jointly learns homophilic and he

Molei Qin, Xinyu Cai, Yewen Li, Haochong Xia

Futures are contracts obligating the exchange of an asset at a predetermined date and price, notable for their high leverage and liquidity and, therefore, thrive in the Crypto market. RL has been widely applied in various quantitative tasks. However, most methods focus on the spot and could not be directly applied to the futures market with high leverage bec

Nathan Buskulic, Luca Calatroni, Lorenzo Rosasco, Silvia Villa

Blind inverse problems arise in many experimental settings where both the signal of interest and the forward operator are (partially) unknown. In this context, methods developed for the non-blind case cannot be adapted in a straightforward manner due to identifiability issues and symmetric solutions inherent to the blind setting. Recently, data-driven approa

Haoming He, Yilin Zhou, Zhongqi He, Yuhao Feng

This letter presents the design and implementation of a compact high-efficiency octave microwave rectifier. A key highlight is the novel segmented impedance matching method, a unique approach that expands the rectifier bandwidth. The diode reactance is initially regulated by a series short-ended microstrip line. Impedance-compensated structures, characterize

Yoav Danieli

We prove a kind of a pumping lemma for languages accepted by one-register alternating finite-memory automata. As a corollary, we obtain that the set of lengths of words in such languages is semi-linear.

Hiroki Takahasi

The irrationality exponent of a real number measures how well that number can be approximated by rationals. Real numbers with irrationality exponent strictly greater than $2$ are transcendental numbers, and form a set with rich fractal structure. We show that this set intersects the limit set of any parabolic iterated function system arising from the backwar

Abd Ullah Khan, Uman Khalid, Trung Q. Duong, Hyundong Shin

A beyond-diagonal reconfigurable intelligent surface (BD-RIS) is an innovative type of reconfigurable intelligent surface (RIS) that has recently been proposed and is considered a revolutionary advancement in wave manipulation. Unlike the mutually disconnected arrangement of elements in traditional RISs, BD-RIS creates cost-effective and simple inter-element

Mingjin Tao, Kailin Jiao, Yawen Li, Wei Liu

The k Nearest Neighbor (kNN) query over moving objects on road networks is essential for location-based services. Recently, this problem has been studied under road networks with distance as the metric, overlooking fluctuating travel costs. We pioneer the study of the kNN problem within dynamic road networks that account for evolving travel costs. Recognizin

Alexander Bennett, Emmet P. Byrne, Jonathan R. Gaunt, Elsa C. Lang

We compute the soft function at NLO and NNLO for a one-parameter family of event shapes we call C-angularity. This family contains C-parameter as a specific choice of the parameter, in close analogy with how conventional angularity contains thrust as a special case. By construction, C-angularity and angularity coincide in the collinear limit such that the an

Yannic Behovits, Alexander L. Chekhov, Amon Ruge, Reza Rouzegar

Ultrafast electric manipulation of magnetic order in solids is critical for the development of future terahertz data processing. A fascinating concept for such high-speed operation is offered in metallic antiferromagnets by N\'eel spin-orbit torque. It should allow one to coherently rotate the ordered spins by simply applying an electric current of suitable

Nilufer K. Bulut

Physics-Informed Neural Networks (PINNs) solve physical systems by incorporating governing partial differential equations directly into neural network training. In electromagnetism, where well-established methodologies such as FDTD and FEM already exist, new methodologies are expected to provide clear advantages to be accepted. Despite their mesh-free nature

David Bolin, Peter Braunsteins, Sebastian Engelke, Raphaël Huser

Intrinsic Gaussian fields are used in many areas of statistics as models for spatial or spatio-temporal dependence, or as priors for latent variables. However, there are two major gaps in the literature: first, the number and flexibility of existing intrinsic models are very limited; second, theory, fast inference, and software are currently underdeveloped f

Yu-Hao Wan, Peng-Yi Liu, Qing-Feng Sun

The quantum anomalous Hall (QAH) effect holds fundamental importance in topological physics and technological promise for electronics. It is generally believed that the QAH effect can only be realized in insulators. In this Letter, we theoretically demonstrate that the QAH effect can also be realized in metallic systems, representing a phase distinct from th

Agostino Di Francescantonio, Alessandra Sabatti, Eleni Prountzou, Maria Antonietta Vincenti

We report the experimental realization of a LiNbO3 metasurface for electro-optic modulation of light polarization in the telecommunication band. High-Q quasi-bound states in the continuum are emploied to enhance the modulation of amplitude and phase of an impinging beam by a driving electric field, leading to efficient polarization rotation and conversion. W

Jun-Yi Shen, Yuan-Chuan Zou

Fast radio bursts (FRBs) are millisecond-duration radio transients whose physical origin remains uncertain. Magnetar-based models, motivated by observed properties such as polarization and large rotation measures, suggest that FRB emission may be modulated by the magnetar spin period. We present an efficient method to search for periodic signals in repeating

Andrea Lucchini, Pablo Spiga

Let $p$ be a prime number. We say that a positive integer $n$ is a Sylow $p$-number if there exists a finite group having exactly $n$ Sylow $p$-subgroups. When $p=2$, every odd integer is a Sylow $2$-number. In contrast, when $p$ is odd, there exist two positive constants $c_p$ and $c_p^\prime$ such that, denoting by $\beta(p,x)$ the number of Sylow $p$-numb

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

We perform an amplitude analysis of the decay $D^0 \to K_S^0 \pi^0 \eta$ and measure its absolute branching fraction to be $(1.016 \pm 0.013_{\text {stat.}} \pm 0.014_{\text {syst.}})\%$. The analysis utilizes $20.3~\mathrm{fb}^{-1}$ of $e^{+}e^{-}$ collision data collected at a center-of-mass energy of 3.773~GeV with the BESIII detector. The branching fract

W. K. Yam, M. Renger, S. Gandorfer, R. Gross

Quantum communication exploits non-classical correlations to achieve efficient and unconditionally secure exchange of information. In particular, the quantum teleportation protocol allows for a deterministic and secure transfer of unknown quantum states by using pre-shared quantum entanglement and classical feedforward communication. Quantum teleportation in

Rafał Masełek, Kazuki Sakurai

The search for physics beyond the Standard Model at the Large Hadron Collider is expanding to include unconventional signatures such as long-lived particles. This mini-review assesses the prospects for detecting electrically charged long-lived particles using the MoEDAL-MAPP experiment. We synthesize findings from recent studies that evaluate sensitivity to

Sunghun Ko, Jinsuk Park

We study Arbitrum's Express Lane Auction (ELA), an ahead-of-time second-price auction that grants the winner an exclusive latency advantage for one minute. Building on a single-round model with risk-averse bidders, we propose a hypothesis that the value of priority access is discounted relative to risk-neutral valuation due to the difficulty of forecasting s

The Anh Nguyen, Triet Huynh Minh Le, M. Ali Babar

The rapid growth of Artificial Intelligence (AI) models and applications has led to an increasingly complex security landscape. Developers of AI projects must contend not only with traditional software supply chain issues but also with novel, AI-specific security threats. However, little is known about what security issues are commonly encountered and how th

Rong Liu, Izaskun Jiménez-Serra, Giuliana Cosentino, Jonathan C. Tan

Filamentary infrared dark clouds (IRDCs) are believed to represent the initial conditions for massive star and cluster formation. We investigate the IRDC G035.39-00.33 using SiO, H13CO+, CH3OH, and CS emission observed with ALMA at 3.5\arcsec\ resolution (0.05 pc). The SiO emission traces shock activity within the cloud, providing insights into current star

Hakan Yildiz, Axel Küpper

Self-Sovereign Identity is a transformative paradigm in digital identity management, empowering individuals with full control over their credentials. However, the coexistence of diverse SSI ecosystems, such as the European Digital Identity and the European Blockchain Services Infrastructure, poses significant challenges for cross-ecosystem interoperability d

Xiamiao Zhao, Yiyan Zhan, Mei Lu

The well-known Erd\H{o}s-Gallai Theorem gave the Tur\'an number of paths. Bushaw and Kettle generalized this result to consider the Tur\'an number of disjoint paths. Since then, many studies are focused on the Tur\'an number of linear forest. For a graph $F$, an $r$-uniform hypergraph $\mathcal{H}$ is a $\text{Berge-} F$ if there is a bijection $\phi: E(F)\t

Lorenz Bielefeld, Paul Zheng, Oner Hanay, Yao Zhu

Federated learning (FL) has been considered a promising privacy preserving distributed edge learning framework. Over-the-air computation (AirComp) leveraging analog transmission enables the aggregation of local updates directly over-the-air by exploiting the superposition properties of wireless multiple-access channels, thereby alleviating the communication

Mohammad Nasirzadeh, Jafar Tahmoresnezhad, Parviz Rashidi-Khazaee

Log anomaly detection is crucial for preserving the security of operating systems. Depending on the source of log data collection, various information is recorded in logs that can be considered log modalities. In light of this intuition, unimodal methods often struggle by ignoring the different modalities of log data. Meanwhile, multimodal methods fail to ha

Le Shen, Qian Qiao, Tan Yu, Ke Zhou

Deploying massive diffusion models for real-time, infinite-duration, audio-driven avatar generation presents a significant engineering challenge, primarily due to the conflict between computational load and strict latency constraints. Existing approaches often compromise visual fidelity by enforcing strictly unidirectional attention mechanisms or reducing mo

Yifan Xuan, Fabo Feng, Zhensen Fu, Shilong Liao

Chinese Space Station Telescope (CSST), which will begin its scientific operations around 2027, is going to survey the sky area of the median-to-high Galactic latitude and median-to-high ecliptic latitude. The high astrometric precision of the CSST Survey Camera for faint objects enables the detection of a number of giant planets and brown dwarfs around M-dw

Shuangyang Li, Melda Yuksel, Tongyang Xu, Shinya Sugiura

Future wireless networks are expected to deliver ultra-high throughput for supporting emerging applications. In such scenarios, conventional Nyquist signaling may falter. As a remedy, faster-than-Nyquist (FTN) signaling facilitates the transmission of more symbols than Nyquist signaling without expanding the time-frequency resources. We provide an accessible

Leqian Chen, Nick E. Mavromatos, Sarben Sarkar

The conjecture by two of the authors (N.E.M. and S.S.) that a \cPT-symmetric phase plays a role in understanding singular renormalisation group (RG) flows for a Chern-Simons (CS) gauge theory of axions, has been reexamined and significantly improved. We have used the more complete Wetterich equation, which includes gravitational couplings in a systematic way

Xiao Ma, Mohammad Hasyim Taufik, Tariq Alkhalifah

Velocity model building serves as a crucial component for achieving high precision subsurface imaging. However, conventional velocity model building methods are often computationally expensive and time consuming. In recent years, with the rapid advancement of deep learning, particularly the success of generative models and neural operators, deep learning bas

Yifei Li, Haoyuan He, Yu Zheng, Bingyao Yu

The accessibility surge and abuse risks of user-friendly image editing models have created an urgent need for generalizable, up-to-date methods for Image Manipulation Detection and Localization (IMDL). Current IMDL research typically uses cross-dataset evaluation, where models trained on one benchmark are tested on others. However, this simplified evaluation

Łukasz Sikorski, Albert Łukasik, Jacek Matulewski, Arkadiusz Gut

The attitudes of today's students toward generative AI (GenAI) will significantly influence its adoption in the workplace in the years to come, carrying both economic and social implications. It is therefore crucial to study this phenomenon now and identify obstacles for the successful implementation of GenAI in the workplace, using tools that keep pace with

Tobias Stähle, Matthijs Jansen op de Haar, Sophia Boyer, Rita Sevastjanova

Mixed-initiative visual analytics (VA) systems, where human and artificial intelligence (AI) agents collaborate as equal partners during analysis, represented a paradigm shift in human-computer interaction. With recent advances in AI, these systems have seen an increase in sophisticated software agents that have improved task planning, reasoning, and complet

Valentin A. Milichko, Ekaterina Gunina, Nikita Kulachenkov, Maxime Vergès

Order versus disorder in the structure of materials plays a key role in the theoretical prediction of their properties. However, this structural description appears to be ineffective for new families of materials such as high entropy alloys (HEAs), which combine crystallographic order with chemical disorder. Here, we demonstrate for five-element HEAs as pure

Shuyuan Lin, Mengtin Lo, Haosheng Chen, Yanjie Liang

Two-view correspondence learning is a key task in computer vision, which aims to establish reliable matching relationships for applications such as camera pose estimation and 3D reconstruction. However, existing methods have limitations in local geometric modeling and cross-stage information optimization, which make it difficult to accurately capture the geo

Yilun Luo, Huaqing Zheng, Haoqian Meng, Wenyuan Liu

Huawei's openPangu-Embedded-1B and openPangu-Embedded-7B are variants of the openPangu large language model, designed for efficient deployment on Ascend NPUs. The 7B variant supports three distinct Chain-of-Thought (CoT) reasoning paradigms, namely slow_think, auto_think, and no_think, while the 1B variant operates exclusively in the no_think mode, which emp

Cehua Yang, Dongyu Xiao, Junming Lin, Yuyang Song

The advancement of Text-to-SQL systems is currently hindered by the scarcity of high-quality training data and the limited reasoning capabilities of models in complex scenarios. In this paper, we propose a holistic framework that addresses these issues through a dual-centric approach. From a Data-Centric perspective, we construct an iterative data factory th

Jidu Yu, Bodhinanda Chandra, Christopher Wilkes, Jidong Zhao

The flow properties of fresh concrete are critical in the construction industry, as they directly affect casting quality and the durability of the final structure. Although non-Newtonian fluid models, such as the Bingham model, are widely used to model these flow properties, they often fail to capture key phenomena, including flow stoppage, and frequently re

Tommaso Taddei, Xuejun Xu, Lei Zhang

We propose a model order reduction framework for incompressible fluid-structure interaction (FSI) problems based on high-order implicit Runge-Kutta (IRK) methods. We consider separate reduced spaces for fluid velocity, fluid pressure and solid displacement; we enrich the velocity space with supremizer modes to ensure the inf-sup stability of the fluid subpro

Duan-Peng Ling, Wenlong Zhang

We investigate the statistical recovery of solutions to first-kind Fredholm integral equations with discrete, scattered, and noisy pointwise measurements. Assuming the forward operator's range belongs to the Sobolev space of order $m$, which implies algebraic singular-value decay $s_j\le Cj^{-m}$, we derive optimal upper bounds for the reconstruction error i

Chang-Yu Shen, Shuai Yin, Zi-Xiang Li

Characterizing universal entanglement features in higher-dimensional quantum matter is a central goal of quantum information science and condensed matter physics. While the subleading corner terms in two-dimensional quantum systems encapsulate essential universal information of the underlying conformal field theory, our understanding of these features remain

Chandrasekhar Medipati, Sivakumar Deivandren, Raghuraman N Govardhan

In our previous study [Medipati \textit{et al}., (2025) \textit{J. Fluid Mech}. \textbf{1014}, A34] \cite{medipati2025elliptic}, a detailed experimental investigation is performed on the elliptical liquid jets in a supersonic cross-flow ($M_{\infty}$ = 2.5), focusing on the effect of orifice aspect ratio ($AR$ = spanwise dimension/streamwise dimension) on th

Ho-Sik Lee, Jihoon Ok, Kyeong Song

We consider a class of nonlinear integro-differential equations whose leading operator is obtained as a superposition of $(-\Delta_{p})^{s}$ and $(-\Delta_{p})^{t}$, where $0<s<t<1<p<\infty$, weighted via two possibly degenerate coefficients $a(\cdot,\cdot),b(\cdot,\cdot) \ge 0$. We prove local boundedness and H\"older regularity of its weak solutions under

Louis Libat, Can Selçuk, Eric Chénier, Vincent Le Chenadec

We present a space-time extension of a conservative Cartesian cut-cell finite-volume method for two-phase diffusion problems with prescribed interface motion. The formulation follows a two-fluid approach: one scalar field is solved in each phase with discontinuous material properties, coupled by sharp interface conditions enforcing flux continuity and jump l

Michael S. Ackermann, Sean Reiter, Lloyd N. Trefethen

Using recently developed algorithms, we compute and compare best $L^2$ and $L^\infty$ rational approximations of analytic functions on the unit disk. Although there is some theory for these problems going back decades, this may be the first computational study. To compute the $L^2$ best approximations, we employ a new formulation of TF-IRKA in barycentric fo

Xin Zhang, Yang Cao, Baoxing Wu, Xinyi Chen

Large Language Models (LLMs) have achieved strong performance across a wide range of natural language processing tasks in recent years, including machine translation, text generation, and question answering. As their applications extend to increasingly complex scenarios, however, LLMs continue to face challenges in tasks that require deep reasoning and logic

Ágnes Backhausz, Villő Csiszár, Balázs Csegő Kolok, Damján Tárkányi

When opinion spread is studied, peer pressure is often modeled by interactions of more than two individuals (higher-order interactions). In our work, we introduce a two-layer random hypergraph model, in which hyperedges represent households and workplaces. Within this overlapping, adaptive structure, individuals react if their opinion is in majority in their

Nilin Abrahamsen

This note introduces Isometric Policy Optimization (ISOPO), an efficient method to approximate the natural policy gradient in a single gradient step. In comparison, existing proximal policy methods such as GRPO or CISPO use multiple gradient steps with variants of importance ratio clipping to approximate a natural gradient step relative to a reference policy

Umutcan Salman, Michele Lombardi, Francesco Ciardiello, Riccardo Saulle

We ask what the reallocation of indivisible objects reveals about the market that produced it. A central authority assigns the objects, after which recipients exchange them among themselves. Preferences are never observed, and individuals of the same type have identical preferences. A reallocation is rationalizable as Pareto efficient and individually ration

Hemant Prasad, Jan T. Sobczyk, Rwik Dharmapal Banerjee, J. Luis Bonilla

Recent experimental data from MINERvA on transverse kinematics observables across four different nuclear targets - carbon, oxygen, iron, and lead - have been utilized to refine the modeling of final state interaction effects in the NuWro Monte Carlo neutrino event generator. For this purpose, we have developed an event reweighting tool for future application

L. Delzescaux, D. Mouhanna

We investigate the effects of thermal fluctuations in graphene bilayers by means of a nonperturbative renormalization group (NPRG) approach, following the pioneering work of Mauri et al. [Phys. Rev. B 102, 165421 (2020)] based on a self-consistent screening approximation (SCSA). We consider a model of two continuum polymerized membranes, separated by a dista

Selçuk Kayacan

We propose a functorial framework for persistent homology based on finite topological spaces and their associated posets. Starting from a finite metric space, we associate a filtration of finite topologies whose structure maps are continuous identity maps. By passing functorially to posets and to order complexes, we obtain persistence modules without requiri

Hai Duong Nguyen, Xuan-The Tran

Deep learning has achieved strong performance for electrocardiogram (ECG) classification within individual datasets, yet dependable generalization across heterogeneous acquisition settings remains a major obstacle to clinical deployment and longitudinal monitoring. A key limitation of many model architectures is the implicit entanglement of morphological wav

Bingru Zhao, Mingshang Hu

In this paper, we study the Backward stochastic Volterra integral equation driven by G-Brownian motion (G-BSVIE). By adopting a different backward iteration method, we construct the approximating sequences on each local interval. With the help of G-stochastic analysis techniques and the monotone convergence theorem, the existence, uniqueness, and continuity

Peiting Xie, Xiangjun Zai, Yanping Wu, Xiaoyang Wang

Reachability in hypergraphs is essential for modeling complex groupwise interactions in real-world applications such as co-authorship, social network, and biological analysis, where relationships go beyond pairwise interactions. In this paper, we introduce the notion of s-reachability, where two vertices are s-reachable if there exists a sequence of hyperedg

Alexander Serov

This article proposes a research and development direction that would lead to the creation of next-generation intelligent technical systems. A distinctive feature of these systems is their ability to undergo evolutionary change. Cognitive architectures are now one of the most promising ways to create Artificial General Intelligence systems. One of the main p

Raven Beutner, Bernd Finkbeiner

Hyperproperties are system properties that relate multiple execution traces and commonly occur when specifying information-flow and security policies. Logics like HyperLTL utilize explicit quantification over execution traces to express temporal hyperproperties in reactive systems, i.e., hyperproperties that reason about the temporal behavior along infinite

Jiafeng Liang, Hao Li, Chang Li, Jiaqi Zhou

Memory serves as the pivotal nexus bridging past and future, providing both humans and AI systems with invaluable concepts and experience to navigate complex tasks. Recent research on autonomous agents has increasingly focused on designing efficient memory workflows by drawing on cognitive neuroscience. However, constrained by interdisciplinary barriers, exi

Mirza Karamehmedović, Pierre Maréchal, Martin Sæbye Carøe, Lara Baalbaki

We extend the classical deconvolution framework in Rn to the case with a pseudodifferential-like solution operator with a symbol depending on both the base and cotangent variable. Our framework enables deconvolution with spatially varying resolution while maintaining a set global stability, and it additionally allows rather general distributional convolution

Zhan Cao, Jin-Lei Yang, Ti-Bin Hou, Tai-Fu Feng

In this work, we analyze the Higgs boson decay channels, specifically, $h{\rightarrow}\gamma\gamma$, $h{\rightarrow} VV^*$ (with $V=Z,W$), and $h{\rightarrow} f\bar{f}$ (for $f=b,c,\tau$) within the flavor-dependent $U(1)_F$ model (FDM). We also investigate processes induced by flavor-changing neutral currents, including the decays $\bar B \to X_s\gamma$ and